Machine Learning-Assisted Raman Spectroscopy and SERS for Bacterial Pathogen Detection: Clinical, Food Safety, and Environmental Applications

Chemosensors, Год журнала: 2024, Номер 12(7), С. 140 - 140

Опубликована: Июль 15, 2024

Detecting pathogenic bacteria and their phenotypes including microbial resistance is crucial for preventing infection, ensuring food safety, promoting environmental protection. Raman spectroscopy offers rapid, seamless, label-free identification, rendering it superior to gold-standard detection techniques such as culture-based assays polymerase chain reactions. However, its practical adoption hindered by issues related weak signals, complex spectra, limited datasets, a lack of adaptability characterization bacterial pathogens. This review focuses on addressing these with recent breakthroughs enabled machine learning (ML), particularly deep methods. Given the regulatory requirements, consumer demand safe products, growing awareness risks pathogens, this study emphasizes pathogen in clinical, settings. Here, we highlight use convolutional neural networks analyzing clinical data surface enhanced sensitizing early rapid pathogens safety potential risks. Deep methods can tackle adequate datasets across diverse samples. We pending future research directions needed accelerating real-world impacts ML-enabled diagnostics accurate diagnosis surveillance critical fields.

Язык: Английский

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Detection of Molecular Vibrations of Shigella Pathogenic Gram-negative Bacterium with Surface Enhanced Raman Spectroscopy (SERS) Biosensors and Investigation of its Antibacterial Activity with Silver Nanoparticles Prepared by the Tollens Method in a Laboratory Environment

BioNanoScience, Год журнала: 2024, Номер 14(3), С. 2750 - 2761

Опубликована: Июль 18, 2024

Язык: Английский

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Study of interaction in dual-species biofilm of Candida glabrata and Klebsiella pneumoniae co-isolated from peripheral venous catheter using Raman characterization mapping and machine learning algorithms

Microbial Pathogenesis, Год журнала: 2025, Номер 199, С. 107280 - 107280

Опубликована: Янв. 5, 2025

Язык: Английский

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Discrimination of Benign and Malignant Thyroid Nodules through Comparative Analyses of Human Saliva Samples via Metabolomics and Deep-Learning-Guided Label-free SERS

ACS Applied Materials & Interfaces, Год журнала: 2025, Номер unknown

Опубликована: Янв. 8, 2025

Thyroid nodules are a very common entity. The overall prevalence in the populace is estimated to be around 65–68%, among which small portion (less than 5%) malignant (cancerous). Therefore, it important discriminate benign thyroid from nodules. In this study, an equal number of participants with and (N = 10/group) were recruited. Saliva samples collected each participant, SERS spectra acquired, followed by validation using metabolomics approach. An additional patients 40/group) recruited construct diagnostic models. performance various machine learning (ML) algorithms was assessed multiple evaluation metrics. Finally, reliability optimal model tested blind test data 10/group for nodules). results showed consistent trend between metabolic profile metabolites identified through MS analysis. Multi-ResNet algorithm optimal, achieving 95% accuracy sample discrimination. Additionally, sets yielded 83%. summary, deep-learning-guided technique holds great potential accurate discrimination via human saliva samples, facilitates noninvasive diagnosis clinical settings.

Язык: Английский

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How can surface-enhanced Raman spectroscopy improve diagnostics for bacterial infections?

Nanomedicine, Год журнала: 2025, Номер unknown, С. 1 - 6

Опубликована: Фев. 17, 2025

Currently, bacterial infection is still a major global health issue. Although antibiotics have been widely used to control and treat infections, the overuse misuse of led widespread antimicrobial resistance among many pathogens. Therefore, reducing infections through rapid accurate diagnostics crucial for public health. Traditional microbiological detection methods limitations such as poor selectivity, high complexity, excessive time consumption, highlighting urgent need develop efficient sensitive diagnosis methods. Surface-enhanced Raman spectroscopy (SERS), an emerging technique in clinical settings, holds promising future identification due its rapid, nondestructive, cost-effective nature. This invited special report discusses application SERS technology using pure culture, samples, single-cell analysis. Current challenges prospects are also addressed with in-depth discussion.

Язык: Английский

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Simple and sensitive SERS platform for Staphylococcus aureus one-pot determination by photoactivated CRISPR/Cas12a cascade system and core–shell DNA tetrahedron@AuNP@Fe3O4 reporter

Microchimica Acta, Год журнала: 2025, Номер 192(4)

Опубликована: Март 18, 2025

Язык: Английский

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A Review on Modulation of Gut Microbiome Interaction for the Management of Shrimp Aquaculture and Proposal of the Introduction of Deep Learning-Based Approach for Shrimp Disease Detection

The Microbe, Год журнала: 2025, Номер unknown, С. 100299 - 100299

Опубликована: Март 1, 2025

Язык: Английский

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Rapid and Label-free Detection of Aflatoxin B1 in Peanut Oil Using Surface-Enhanced Raman Spectroscopy Combined with Deep Learning Model

LWT, Год журнала: 2025, Номер unknown, С. 117738 - 117738

Опубликована: Апрель 1, 2025

Язык: Английский

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Rapid and simple sensing of acetylcholinesterase and inhibition activity by utilizing a portable Raman spectrometer

Talanta, Год журнала: 2025, Номер unknown, С. 128086 - 128086

Опубликована: Апрель 1, 2025

Язык: Английский

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Rapid discrimination between wild and cultivated Ophiocordyceps sinensis through comparative analysis of label-free SERS technique and mass spectrometry

Current Research in Food Science, Год журнала: 2024, Номер 9, С. 100820 - 100820

Опубликована: Янв. 1, 2024

Ophiocordyceps sinensis is a genus of ascomycete fungi that has been widely used as valuable tonic or medicine. However, due to over-exploitation and the destruction natural ecosystems, shortage wild O. resources led an increase in artificially cultivated sinensis. To rapidly accurately identify molecular differences between sinensis, this study employs surface-enhanced Raman spectroscopy (SERS) combined with machine learning algorithms distinguish two categories. Specifically, we collected SERS spectra for validated metabolic profiles using Ultra-Performance Liquid Chromatography coupled Orbitrap High-Resolution Mass Spectrometry (UPLC-Orbitrap-HRMS). Subsequently, constructed classifiers mine potential information from spectral data, feature importance map determined through optimized algorithm. The results indicate representative characteristic peaks are consistent metabolites identified metabolomics analysis, confirming feasibility method. support vector (SVM) model achieved most accurate efficient capacity discriminating (accuracy = 98.95%, 5-fold cross-validation 98.38%, time 0.89s). revealed subtle compositional Taken together, these expected enable application quality control raw materials, providing foundation rapid identification their origin.

Язык: Английский

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